(X, y, n_queries)
| 56 | |
| 57 | @timeit() |
| 58 | def libact_EER(X, y, n_queries): |
| 59 | y_train = np.array([None for _ in range(len(y))]) |
| 60 | y_train[0], y_train[50], y_train[100] = 0, 1, 2 |
| 61 | libact_train_dataset = Dataset(X, y_train) |
| 62 | libact_full_dataset = Dataset(X, y) |
| 63 | libact_learner = LogisticRegressionLibact(solver='liblinear', n_jobs=1, multi_class='ovr') #SVM(gamma='auto', probability=True) |
| 64 | libact_qs = EER(libact_train_dataset, model=libact_learner, loss='01') |
| 65 | libact_labeler = IdealLabeler(libact_full_dataset) |
| 66 | libact_learner.train(libact_train_dataset) |
| 67 | |
| 68 | for _ in range(n_queries): |
| 69 | query_idx = libact_qs.make_query() |
| 70 | query_label = libact_labeler.label(X[query_idx]) |
| 71 | libact_train_dataset.update(query_idx, query_label) |
| 72 | libact_learner.train(libact_train_dataset) |
| 73 | |
| 74 | |
| 75 | @timeit() |
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